Bibliographic record
Abstract
In the comic monologue The Lion and Albert, Albert’s mother declines the offer of compensation for the death of her son, preferring that those responsible be brought to justice. In the field of white-collar crime, ‘justice’ is a more challenging concept. Lord Hewart CJ remarked almost a century ago that ‘justice should not only be done, but should manifestly and undoubtedly be seen to be done’. Two decades later, Sutherland defined white-collar crime as ‘a crime committed by a person of respectability and high social status in the course of his occupation’. Half a century later, Clarkson concluded that ‘it is the individuals within the company that are most amenable to deterrence and that in order to deter a company the fines would need to be massive. A company will only be deterred if its expected costs exceed its expected gains.’ There is a general expectation that those responsible for corporate crime will be brought to justice. However, contemporary trends in white-collar crime favour financial regulation and/or settlements ‘negotiated’ with prosecutors over prosecutions. In addition to criminal law, many firms and individuals in the United Kingdom (UK) are subject to supervision by a statutory independent regulator, the Financial Conduct Authority (FCA). A key feature of the FCA is its ability to levy its own regulatory fines without having to apply to a court. Criminal prosecutions, generally, lie in the hands of a specialist prosecutor, the Serious Fraud Office (SFO). The SFO has the recent benefit of Deferred Prosecution Agreements (DPA). The DPA, a court-supervised process, enables the SFO to broker outcomes to a prosecution with a commercial undertaking rather than taking a case to trial. This follows the approach of the United States (US), long considered the international leader in tackling financial crime and now followed by other jurisdictions such as Australia, Canada and Argentina. This chapter considers the usage of DPAs, initially contrasting with the approach of the US and holding individuals to account.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.053 |
| Scholarly communication | 0.026 | 0.047 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.029 | 0.039 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".